The NLCD 2001 is created by partitioning the U.S. into mapping zones. A total of 66 mapping zones were delineated within the conterminous U.S. based on ecoregion and geographical characteristics, edge matching features and the size requirement of Landsat mosaics. Mapping zone 36 encompasses whole or portions of several states, including the states of Texas. Questions about the NLCD mapping zone 36 can be directed to the NLCD 2001 land cover mapping team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.
Post-Processing Steps: None Known Problems: None Spatial Filters: None
Conceptually, the descriptive tree is a classification tree generated by using the final minimum-map- unit land cover product (1 acre) as training data, and Landsat and other ancillary data as predictors. The goal of the descriptive tree is to summarize the effects of boosted trees (10 sequential classification trees) into a single condensed decision tree that can be used as a diagnostic tool for the classification process. This descriptive tree can be used to assess the relative importance of each of the input data sets on each land cover class. Such information may also be useful to customize the minimum-mapping-unit classification to meet a user's specific needs through raster modeling. Descriptive trees usually capture 60 to 80% of the information from the original land cover data.
The leaf or terminal nodes of the descriptive tree are assigned to sequential numbers (called node numbers) and mapped across the entire mapping zone on a pixel-by-pixel basis. These node numbers can then be matched with the various conditional statements associated with each respective terminal node. This spatial layer appears similar to a cluster map, but is the result of a supervised classification - not an unsupervised clustering. This node map can potentially be used as input by users to customize NLCD land cover, by linking the spatial extent of an individual node with the rules of the conditional statement.
The Land Cover spatial classification confidence data layer is provided to users to help determine the per-pixel spatial confidence of the NLCD 2001 land cover prediction from the descriptive tree. The C5 algorithm produces an estimate (a value between 0% and 100%) that indicates the confidence of rule predictions at each node based on the training data. This spatial confidence map should be considered as only one indicator of relative reliability of the land cover classification, rather than a precise estimate. Users should be aware that this estimate is made based on only training data, and is derived from a generalized descriptive decision tree that reproduces the final land cover data. However, this layer provides valuable insight for a user to determine the risk or confidence they choose to place in each pixel of land cover.
A logic statement from a descriptive tree classification describes each classification rule for each classified pixel. An example of the logic statement follows:
IF tasseled-cap wetness > 140 and imperviousness = 0 and canopy density < 4, then classify as Water
This logic file can be used in combination with the spatial node map to identify classification logic and allow modifications of the classification based on user's knowledge and/or additional data sets.
Additional information may be found at <http://www.mrlc.gov/mrlc2k_nlcd.asp>.
Acquisition dates of Landsat ETM+ (TM) scenes used for land cover classification in zone 36 are as follows:
SPRING-
Index 1 for Path 25/Row 39 on 01/15/02 = Scene_ID 7025039000201550
Index 1 for Path 25/Row 40 on 01/15/02 = Scene_ID 7025040000201550
Index 2 for Path 26/Row 38 on 02/23/02 = Scene_ID 7026038000205450
Index 2 for Path 26/Row 39 on 02/23/02 = Scene_ID 7026039000205450
Index 2 for Path 26/Row 40 on 02/23/02 = Scene_ID 7026040000205450
Index 2 for Path 26/Row 41 on 02/23/02 = Scene_ID 7026041000205450
Index 3 for Path 26/Row 42 on 01/06/02 = Scene_ID 7026042000200650
Index 4 for Path 27/Row 39 on 01/13/02 = Scene_ID 7027039000201350
Index 5 for Path 27/Row 40 on 02/03/01 = Scene_ID 5027040000103410
Index 6 for Path 27/Row 41 on 03/15/01 = Scene_ID 7027041000107450
Index 7 for Path 27/Row 42 on 02/17/03 = Scene_ID 7027042000304850
Index 9 for Path 28/Row 40 on 03/09/02 = Scene_ID 7028040000206850
Index 8 for Path 28/Row 41 on 02/21/02 = Scene_ID 7028041000205250
Index10 for Path 29/Row 40 on 02/25/01 = Scene_ID 7029040000105650
LEAF ON (Summer)-
Index 4 for Path 25/Row 39 on 08/03/02 = Scene_ID 5025039000221510
Index 4 for Path 25/Row 40 on 08/03/02 = Scene_ID 5025040000221510
Index 3 for Path 26/Row 38 on 05/14/02 = Scene_ID 7026038000213450
Index 1 for Path 26/Row 39 on 07/22/01 = Scene_ID 5026039000120310
Index 1 for Path 26/Row 40 on 07/22/01 = Scene_ID 5026040000120310
Index 1 for Path 26/Row 41 on 07/22/01 = Scene_ID 5026041000120310
Index 2 for Path 26/Row 42 on 06/15/02 = Scene_ID 7026042000216650
Index 5 for Path 27/Row 39 on 07/21/01 = Scene_ID 7027039000120250
Index 5 for Path 27/Row 40 on 07/21/01 = Scene_ID 7027040000120250
Index 5 for Path 27/Row 41 on 07/21/01 = Scene_ID 7027041000120250
Index 5 for Path 27/Row 42 on 07/21/01 = Scene_ID 7027042000120250
Index 6 for Path 28/Row 40 on 07/12/01 = Scene_ID 7028040000119350
Index 6 for Path 28/Row 41 on 07/12/01 = Scene_ID 7028041000119350
Index 7 for Path 29/Row 40 on 05/03/02 = Scene_ID 7029040000212350
LEAF-OFF (Fall)-
Index 3 for Path 25/Row 39 on 10/06/99 = Scene_ID 7025039009927950
Index 3 for Path 25/Row 40 on 10/06/99 = Scene_ID 7025040009927950
Index 1 for Path 26/Row 38 on 09/29/00 = Scene_ID 7026038000027350
Index 1 for Path 26/Row 39 on 09/29/00 = Scene_ID 7026039000027350
Index 1 for Path 26/Row 40 on 09/29/00 = Scene_ID 7026040000027350
Index 1 for Path 26/Row 41 on 09/29/00 = Scene_ID 7026041000027350
Index 2 for Path 26/Row 42 on 09/27/02 = Scene_ID 5026042000227010
Index 4 for Path 27/Row 39 on 10/25/01 = Scene_ID 7027039000129850
Index 5 for Path 27/Row 40 on 09/04/00 = Scene_ID 7027040000024850
Index 5 for Path 27/Row 41 on 09/04/00 = Scene_ID 7027041000024850
Index 6 for Path 27/Row 42 on 09/07/01 = Scene_ID 7027042000125050
Index10 for Path 28/Row 40 on 11/20/02 = Scene_ID 7028040000232450
Index 9 for Path 28/Row 40 on 10/16/01 = Scene_ID 7028040000128950
Index 8 for Path 28/Row 41 on 11/01/01 = Scene_ID 7028041000130550
Index 7 for Path 29/Row 40 on 09/18/00 = Scene_ID 7029040000026250
Field-Collected Data- The goals of the field data collection were to sample the diversity of the landscape, within the classes, and among image dates. Classes that would be more difficult to collect from air photos were targeted for field data collection. To meet these goals, Sanborn stratified the image into spectral clusters and located the field sites throughout the study area based on these. In addition to these pre-arranged sites, Sanborn collected points while driving between locations. Due to limited time and accessibility, not all polygons were assessed in the field. Those that we did not visit on the ground were labeled with digital orthophotographs or Ikonos data if it was available. Both training and validation points were collected together. See the accuracy assessment section to see how the points were split into training and validation points. Sanborn used laptop computers and GPS (Global Positioning System) to correctly locate field points on the TM imagery. Software downloaded from the Minnesota's Department of Natural Resources (DNR) was used to connect the Garmin GPS to the laptop computer and ESRI's ArcView software. Sanborn's programmer developed an ArcMap application that allowed entry of location and field notes with a click of the mouse. These data were stored in a shapefile. Items that were collected were: Land Cover characterization Special conditions and remarks Photograph Number Date/time location The data and equipment used for the fieldwork are as follows: Ancillary datasets: TIGER 2000 NLCD - mosaicked into zones State road map and Delorme state atlas www.delorme.com Hardware: with ArcView/ArcGIS and data GARMIN GPS modules and external antennae, redundant data cables Cameras devices (Floppy Drives, CD Burners, external HDD) Extra batteries (lap-top and GPS) Mobile phones System backup CD's with data and software Compass notebooks with instructions and road maps with pre-determined routes Wetland and Vegetation Field Guides Imagery: Image data for each zone Initial classifications Classification: After the field points for training were collected, they were combined with photo-interpreted points and used as the dependent variable in a CART classification approach. Many layers tested as independent layers. They included three dates of spectral and tasseled cap imagery, DEM, slope, aspect, texture, band indices (NDVI, Moisture, NDVI-Green), shape indices fractal dimension, compactness, convexity, and form), Census data (housing and population density). Statistical analyses and visual inspection of the output was used to eliminate data that was redundant or not useful in the classification. Additional training points were added to help reduce some of the confusion between classes. The rough classification was created at the end of this process using only the CART discrete decision-tree software. A provisional classification was produced by applying spatial models using ancillary data to the rough classification. The final automated map was then edited using hand editing techniques while using high resolution imagery from as reference data. Independently, of this process, NOAA produced percent impervious data layers for Zone 36. This layer was developed from Regression Tree and used impervious classifications from IKONOS imagery to predict pixel level percent impervious at the TM pixel level. The continuous percent impervious data was thresholded to produce the developed categories and imbedded into the final map. Attributes for this product are as follows: 0 Background 1 Unclassified (Cloud, Shadow, etc) 2 High Intensity Developed 3 Medium Intensity Developed 4 Low Intensity Developed 5 Open Spaces Developed 6 Cultivated Land 7 Pasture/Hay 8 Grassland 9 Deciduous Forest 10 Evergreen Forest 11 Mixed Forest 12 Scrub/Shrub 13 Palustrine Forested Wetland 14 Palustrine Scrub/Shrub Wetland 15 Palustrine Emergent Wetland 16 Estuarine Forested Wetland 17 Estuarine Scrub/Shrub Wetland 18 Estuarine Emergent Wetland 19 Unconsolidated Shore 20 Bare Land 21 Water 22 Palustrine Aquatic Bed 23 Estuarine Aquatic Bed 24 Tundra (N/A) 25 Snow/Ice (N/A) Ancillary Datasets: Non-TM image datasets used are DEM (Digital Elevation Model), slope, aspect, positional index, NWI, NLCD, TIGER2000, field-collected points, photo-interpreted points, zone 36 GAP reclassified by NOAA (Gap Analysis Program),Census data (housing and population density), Ecoregions. QA/QC Process: There were several QA/QC steps involved in the creation of this product. First, there was an internal QA/QC. This was done by viewing the classification frame-by-frame along with the TM imagery, the classification, and high resolution reference imagery. NOAA staff completed a similar review and provided both general and point comments.
The completed single pixel product was then generalized to a 1 acre (approximately 5 ETM+ 30 m pixel patch) minimum mapping unit product using a "smart eliminate" algorithm. This aggregation program subsumes pixels from the single pixel level to a 5- pixel patch using a queens algorithm at doubling intervals. The algorithm consults a weighting matrix to guide merging of cover types by similarity, resulting in a product that preserves land cover logic as much as possible.